Update dataset card to English documentation

README.md

@@ -8,6 +8,8 @@ tags:
   - core-exercises
   - physical-therapy
   - xgboost
+  - biomechanics
+  - rehabilitation
 size_categories:
   - 1K<n<10K
 configs:
@@ -17,40 +19,82 @@ configs:
         path: data.csv
 ---
 
-# CoreExercise5K Dataset
+# CoreExercise5K: A Machine Learning Dataset for Core Exercise Assessment
 
-Bu veri seti, **Makine Öğrenmesi Tabanlı Core Egzersiz Denetim Sistemi** projesinde MediaPipe Pose Estimation modeli kullanılarak çıkarılmış 3 boyutlu eklem koordinatları ve açı özniteliklerini içermektedir.
+**CoreExercise5K** is a tabular dataset composed of 3D skeletal landmark coordinates and joint angles extracted using MediaPipe Pose. It was curated for machine-learning-based physical therapy and real-time core exercise posture auditing.
 
-## 📊 Veri Seti Detayları
+---
+
+## 📌 Dataset Overview
+
+- **Total Samples:** 5,016 rows
+- **Number of Features:** 108 columns (33 MediaPipe pose landmarks $\times$ $x, y, z$ coordinates + derived joint angle attributes)
+- **Target Variable (`label`):** 9 classes (8 core exercise movements + neutral/none)
+- **Modality:** Tabular / Skeletal Landmarks (Normalized 3D coordinates)
+
+---
+
+## 🏋️‍♂️ Class Distribution
+
+| Exercise Class | Sample Count | Percentage |
+| :--- | :--- | :--- |
+| **Bird Dog** | 965 | 19.24% |
+| **Plank** | 830 | 16.55% |
+| **None (Neutral / Inactive)** | 622 | 12.40% |
+| **High Plank** | 579 | 11.54% |
+| **Dead Bug** | 492 | 9.81% |
+| **Glute Bridge** | 479 | 9.55% |
+| **Side Plank** | 416 | 8.29% |
+| **Dart** | 357 | 7.12% |
+| **Knee Plank** | 276 | 5.50% |
+| **Total** | **5,016** | **100%** |
+
+---
+
+## 🔬 Data Collection & Preprocessing
+
+1. **Raw Frame Acquisition:** 920 high-quality frames across diverse subjects performing physiotherapy core exercises were curated.
+2. **Data Augmentation:** An augmentation pipeline applied 6 transformations to each frame:
+   - Original frame
+   - $+15^\circ$ rotation
+   - $-15^\circ$ rotation
+   - Horizontal flip
+   - Flipped with $+15^\circ$ rotation
+   - Flipped with $-15^\circ$ rotation  
+   *(Yielding a theoretical maximum of $920 \times 6 = 5,520$ frames)*
+3. **Landmark Detection & Quality Filtering:** Each augmented frame was passed through the **MediaPipe Pose** model. Frames with occlusions or missing keypoints where MediaPipe failed detection were filtered out, resulting in **5,016 clean, fully labeled keypoint vectors**.
 
-- **Orijinal Görsel Sayısı:** 920 adet kare
-- **Veri Artırma (Data Augmentation):** 6 varyasyon (orijinal, +15° rotasyon, -15° rotasyon, yatay aynalama, aynalanmış +15°, aynalanmış -15°) $\rightarrow$ Teorik $920 \times 6 = 5,520$ kare
-- **Tespit Edilen Geçerli Landmark Sayısı:** MediaPipe eklem tespiti yapabilen net **5,016** örnek (satır)
-- **Öznitelik Sayısı:** 108 sütun (33 MediaPipe vücut landmark x, y, z koordinatları ve türetilmiş açı özellikleri)
-- **Hedef Sınıf (`label`):** 9 sınıf
+---
 
-### Sınıf Dağılımı
+## 💻 Getting Started
 
-| Egzersiz Sınıfı | Örnek Sayısı |
-| :--- | :--- |
-| **Bird Dog** | 965 |
-| **Plank** | 830 |
-| **High Plank** | 579 |
-| **Dead Bug** | 492 |
-| **Glute Bridge** | 479 |
-| **Side Plank** | 416 |
-| **Dart** | 357 |
-| **Knee Plank** | 276 |
-| **None (Nötr)** | 622 |
-| **Toplam** | **5,016** |
+You can load this dataset easily with the Hugging Face `datasets` library or directly with pandas:
 
-## 🚀 Kullanım
+### Using Hugging Face Datasets
 
 ```python
 from datasets import load_dataset
 
+# Load dataset
 dataset = load_dataset("xgboostgod/CoreExercise5K")
 df = dataset["train"].to_pandas()
-print(df.shape)
-# (5016, 108)
+
+print(f"Dataset shape: {df.shape}")
+print(df["label"].value_counts())
 ```
+
+### Using Pandas Directly
+
+```python
+import pandas as pd
+
+url = "https://huggingface.co/datasets/xgboostgod/CoreExercise5K/resolve/main/data.csv"
+df = pd.read_csv(url)
+print(df.head())
+```
+
+---
+
+## 📜 License
+
+This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).